Evidence map›Paper›PMID 35712363›Full record

ArticleData in brief2022

Functional signatures in Great Britain: A dataset.

Krasen Samardzhiev, Martin Fleischmann, Daniel Arribas-Bel, Alessia Calafiore, Francisco Rowe

Abstract read
In one paragraph

Article in Data in brief, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Krasen SamardzhievGeographic Data Science Lab, University of Liverpool, Liverpool, L69 7ZX.
Martin FleischmannGeographic Data Science Lab, University of Liverpool, Liverpool, L69 7ZX.
Daniel Arribas-BelGeographic Data Science Lab, University of Liverpool, Liverpool, L69 7ZX.
Alessia CalafioreGeographic Data Science Lab, University of Liverpool, Liverpool, L69 7ZX.
Francisco RoweGeographic Data Science Lab, University of Liverpool, Liverpool, L69 7ZX.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The spatial distribution of activities and agents within cities, conceptualised as an urban function, profoundly affects how different areas are perceived and lived. This dataset introduces the concept of functional signatures - contiguous areas of a similar urban function delineated based on enclosed tessellation cells (ETC) - and applies it to the area of Great Britain. ETCs are granular spatial units, which capture function based on interpolations from open data inputs stretching from remote sensing to land use, census and points of interest data. The spatial extent of each signature type is defined by grouping ETCs using cluster analysis, based on similarity between their functional profiles, inferred by the data linked to each cell. This approach results in a dataset that reflects urban function as a composite of aspects, rather than a singular use, and is built up from granular spatial units. Furthermore, the underlying data are sourced from available open data products, which together with a method and code fully available, yields a fully reproducible pipeline and makes our dataset and open data product. Both the final classification composed of 17 types of functional signatures and the underlying data collected on the level of enclosed tessellation cells are included in the release and described in this report.

Indexed as

Functional areasGeographic data scienceLand useSpatial dataUrban analytics

Identifiers

PMID35712363
PMCPMC9194701

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.